Shear amplification of orientation in Nylon 11/clay nanocomposites during melt casting and resulting electrical properties

In this study, the effect of organically modified clay on the orientation enhancement in Nylon 11 in melt casting was investigated. Nylon 11 was mixed with 1 and 3 wt% Cloisite 20A using twin screw extrusion and they were cast into films with varying take-up speeds. The addition of clay in Nylon 11 helped increase orientation levels substantially in melt cast films, both as a function of clay concentration as well as take-up speeds. This was primarily due to shear amplification effect caused by the movement of adjacent clay nanoparticles due to the shear flow gradient within the die. At low clay concentrations, the sub-T_m stretchability, and electrical breakdown strength improve as the presence of clay reduces inter/intrachain hydrogen bonding. At higher clay concentrations, both orientation and electrical breakdown levels decrease. The latter is primarily caused by increased percolation path of charge carriers. Nevertheless, clay nanoplatelets were very effective in their role as melt processing aids, as they enhance orientation levels of Nylon 11 thin films by shear amplification effect where they increase local chain orientation of chains trapped between clay platelets while their orientation relaxation is suppressed.     

Improving Brain Tumor Classification with Deep Learning UsingSyntheticData

Deep learning (DL) techniques, which do not need complex pre-processing and feature analysis, are used in many areas of medicine and achieve promising results. On the other hand, in medical studies, a limited dataset decreases the abstraction ability of the DL model. In this context, we aimed to produce synthetic brain images including three tumor types (glioma, meningioma, and pituitary), unlike traditional data augmentation methods, and classify them with DL. This study proposes a tumor classification model consisting of a Dense Convolutional Network (DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers. By comparing models trained on two different datasets, we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network (CycleGAN) on the generalization of DL. One model is trained only on the original dataset, while the other is trained on the combined dataset of synthetic and original images. Synthetic data generated by CycleGAN improved the best accuracy values for glioma, meningioma, and pituitary tumor classes from 0.9633, 0.9569, and 0.9904 to 0.9968, 0.9920, and 0.9952, respectively. The developed model using synthetic data obtained a higher accuracy value than the related studies in the literature. Additionally, except for pixel-level and affine transform data augmentation, synthetic data has been generated in the figshare brain dataset for the first time.